According to the authors, what is behind this graph, and specially behind the gap in GDP growth between advanced and developing (and emerging) economies, is trade liberalisation and fiscal discipline.
Measured by the number of protectionist measures imposed since November 2008, France, Germany, Italy, and the United Kingdom are all on the list of the world’s top 10 most protectionist countries (Evenett, 2013). p. 4
Korea is a historical example of the benefits of reductions of obstacles to trade.
The article makes the interesting point that the stock market tells when austerity measures are good in the long term, and illustrates the idea in the context of Brazil in 1994, where austerity measures were well received in the stock market that showed large, abnormal, returns.
. . . [T]he nature of stock markets and the economies in Africa revealed the reasons for non-causal relationships between stock markets and economic growth in Ghana and Nigeria. The problem of African stock markets is the domination by a single sector, and the often monoproduct economy. Often, the stocks of this sector that account for the greater percent of the GDP are not listed in the domestic stock market, hence, a divorce between the actual performance of the stock market and economic growth. In Ghana, only AngloGold Ashanti, accounts for 70% of market capitalization (Osaze, 2007) while in Nigeria, over 60% of the total market capitalization is accounted for by the Banking sector. The oil and gas sector of the economy of Nigeria and the agricultural sector (cocoa) of that of Ghana are not in their stock markets.
This is a paper that came out in February 2011. It was written by Johan Bollen, Huina Mao, and Xiao-Jun Zeng. This is the abstract:
Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 86.7% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error (MAPE) by more than 6%.
The complete paper is here.
